#13 Tufts (8-5)

avg: 1858.18  •  sd: 45.56  •  top 16/20: 99.4%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
53 Appalachian State Win 15-9 1948.62 Feb 11th Queen City Tune Up1
122 Carnegie Mellon** Win 15-6 1682.27 Ignored Feb 11th Queen City Tune Up1
67 Maryland Win 15-8 1934.84 Feb 11th Queen City Tune Up1
17 South Carolina Win 15-11 2155.04 Feb 11th Queen City Tune Up1
15 North Carolina State Win 11-10 1930.28 Feb 12th Queen City Tune Up1
2 North Carolina Loss 6-15 1538.39 Feb 12th Queen City Tune Up1
3 Brigham Young Loss 10-13 1789.37 Mar 18th Centex 2023
4 Texas Loss 10-11 1896.51 Mar 18th Centex 2023
37 Florida Win 13-10 1869.68 Mar 18th Centex 2023
29 Wisconsin Win 12-9 2012.03 Mar 18th Centex 2023
12 Carleton College Loss 13-14 1738.49 Mar 19th Centex 2023
6 Colorado Loss 11-15 1611.23 Mar 19th Centex 2023
32 Oklahoma Christian Win 15-12 1927.89 Mar 19th Centex 2023
**Blowout Eligible


The uncertainty of the mean is equal to the standard deviation of the set of game ratings, divided by the square root of the number of games. We treated a team’s ranking as a normally distributed random variable, with the USAU ranking as the mean and the uncertainty of the ranking as the standard deviation
  1. Calculate uncertainy for USAU ranking averge
  2. Model ranking as a normal distribution around USAU averge with standard deviation equal to uncertainty
  3. Simulate seasons by drawing a rank for each team from their distribution. Note the teams in the top 16 (club) or top 20 (college)
  4. Sum the fractions for each region for how often each of it's teams appeared in the top 16 (club) or top 20 (college)
  5. Subtract one from each fraction for "autobids"
  6. Award remainings bids to the regions with the highest remaining fraction, subtracting one from the fraction each time a bid is awarded
There is an article on Ulitworld written by Scott Dunham and I that gives a little more context (though it probably was the thing that linked you here)